App Monetization

App Monetization

IMG_4102One of the things that were a part of mobile apps since the early days is the REMOVE ADS button. The idea is simple – ads generate low amounts of revenue per user and getting $0.99 or $1.99 from them is better from the app publisher stand point.

Not showing ads to payers is the standard practice

Even in games that don’t have a specific purchase option around removing ads it became a standard practice to not show ads to depositors. This is based on the same approach that ads yield low amounts of revenue while purchases yield higher amounts.

Rethink what you know – ad whales exist

In recent posts we covered the existence of ad whales. Individuals who generate large amounts of ad revenues for their app publishers. Here is a user who generated $74 in ad revenue in November, and this user generated $52 in December. While these levels of revenue per user are quite rare for ad monetization, they are also quite rare when it comes to in-app purchases.

How many users generate enough ad revenue to level with payers

If we consider how much revenue is generated by a payer – the minimum is $0.7. The lowest purchase by a user is $0.99 and given that Apple and Google take a cut of 30% the publisher gets 70 cents.
Based on the data SOOMLA Traceback is collecting we can check how many users go over the point. How many monetize with ads at least to the same level as payers. The result is that in some games that relay heavily on ads it’s more than 10% of the user base. This is higher than a normal conversion rate to payers. We can also check how many users went over $3.5 which is the publisher share of a more $4.99 purchase by a user. The result is that it’s over 2% in some games.

Rewarded videos offer incentives to users

Let’s start thinking about a different approach. Should we allow any type of advertising to people who paid? One area to consider is the type of advertising in question. Ads that may annoy a paying user could be a bad choice from a user experience perspective but what about incentivized formats such as offer walls and rewarded videos. These formats are loved by users so the question becomes more about optimizing the revenues.

Option 1 – reversed approach

Let’s imagine for a second a complete mirror image of the “no ads for payers” approach. What this means is that we set a threshold of $0.7 and the users who have made at least $0.7 in ad revenue are considered ad-whales. Once we classified someone as an ad-whale, we don’t allow him to make purchases in the game. That would be the reversed approach to the “no ads for payers” approach. If it sounds silly to you – it’s because it is silly. Blocking someone from paying in a game is just nonsense but so is the “no ads for payers” approach. Why block someone from making revenue for you through watching ads?

Option 2 – balanced approach

A more reasonable approach to the problem is to simply allow users constent access to all methods of getting benefits. A user can get benefits by buying them, by watching video ads, or by taking on offers. Since the payout of a video view by a user is normally determined in retrospect, the publisher could apply a model where the rewards are dynamic based on the past payouts received for that user. If such a model is implemented, the publisher can guarentee that the price of getting the benefit is balanced across the different methods the user has for getting them. For example, if the eCPM of a user starts falling after a while, his rewards for watching videos will decrease and he will be more inclined to make purchases. If however, the eCPMs for a specific users are growing over time, the rewards he will get from watching videos will increase and he will have more motivation to keep watching them as opposed to buying something.

Ad measurement tools are becoming a must have

This type of innovative monetization strategies are becoming critical for the survival of game studios. We covered before the increase in CPI rates and how companies needs to adapt to stay relevant. Advanced segmentation and monetization measurement tools that can find the ad whales segment for you are becoming a must have in today’s mobile eco-system.

 

If you want to start measuring your monetization and find ad whales you should check out SOOMLA Traceback – Ad LTV as a Service.

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App Monetization

Kate Uptown is starring the Machine Zone (MZ) ads for their Game of War which has been advertised heavily in the last 24 monthsDemand diversity is a topic not many people discuss in the mobile game monetization forums. To understand it let’s think about the journey or a user through our app. The first time the user watches an ad, the mediation will check what is the ad-network that is on the top of the waterfall today and will have that network serve the ad. The network will normally try to serve the highest yielding campaign they have – why don’t we call the app in the ad Mobile Assault – it will help us refer to it later. In many cases, this user will see the same ad over and over again in the same day and more time in the next day. Having a user see an ad for 100 times these days is not uncommon. This is the demand diversity problem I’m referring to.

Why demand diversity is important

From a user perspective, seeing the same ad over and over is a poor user experience. The first time you are seeing an ad, it could be interesting, cool or even funny. If you have seen the new Clash Royal ads, they are quite amusing. However, nothing is interesting, cool or funny after you have watched it a dozen times already. At that point, it is just annoying.
From an ad effectiveness perspective, showing the same ad over and over is a bad choice as well. It leads to banner blindness so users stop noticing the ad. Most ads today are shown with the purpose of creating installs for tha advertised app and blindness leads to low click rates and conversion rates so less installs are generated.

The business models determine who takes the risk

One of axioms of online advertising is the chart below. Basically it says:

  • In a CPM model the risk is on the advertiser side while the publisher has guarnteed income
  • CPC is the middle ground
  • In a CPA/CPI model the risk is on the publisher side while the advertiser has guarnteed outcome

Illustrative chart showing the risk levels for publishers and advertisers based on the selected business model: CPM, CPC or CPI

The mobile advertising industry today is mostly driven by the CPI model which is a form of CPA meaning that the publishers assume most of the risk. They place ads in their apps hoping to get paid but their monetization is driven by whether or not the users ended up taking additional actions outside of their apps.
So now that we established who has the risk, we also know how is the one that gets heart from the situation. Users who watch the same ad over and over again become blind to it and the publishers’ monetization levels are getting hurt.

Risk and data are normally aligned

In most business situations, the party who is willing to takes the risks is the one with better tools to assess it and mitigate it. For example, in a CPM model, the advertiser assume the risk but they demand transparency about where their ads are being placed and have tools to measure the performance. In mobile app monetization however, the publishers are the one assuming the risks but they are doing so with complete lack of data or measurement tools. More specifically, the publishers are the ones that get hurt from the lack of demand diversity but they actually have no way to measure and manage it.

Mediation platforms are also left in the dark

The parties that are in the perfect position to be the police of demand diversity are the mediation platforms. Publishers are trusting the mediation companies to act as their agents and help them manage things of this sort using their ad-tech expertiese. The problem is that mediation companies are also in the dark about what ad is being shown to the user. They simply call the ad-network SDK as a black box that shows ads but they don’t get any information out.

Ad networks only see their own ads

The only type of company that has information about what ads are shown to the user are the ad-networks. The problem, however, is that each ad-network is only aware of what ads they show. Instead of collaborating and sharing this data between them and be part of the solution they are part of the problem since an ad-network that is not aware of what other ad-networks are showing is likely to show the same popular advertiser again to the user.

Choosing ad networks smartly

App companies often tend to choose ad-networks based on rumors of their projected CPMs or based on how well it worked for their friends. Often, one ad-network will seem better than another in the eyes of the publishers due to their presence in shows and their general brand perception. However, choosing 4 networks that are practically representing the same demand menas making the problem worse. It’s common to see a rewarded video stack that includes Supersonic/Ironsource as the mediation in addition to Vungle, Adcolony, UnityAds and Chartboost as the ad-networks. These networks are considered the best when it comes to rewarded videos for mobile games. The problem here is that thery are all bringing similar types of ads. The chances of a user seeing the same ad over and over again is much higher like that. A smarter strategy for selecting ad partners is to try and figure out how to diversify. SSPs can often bring more diversification through access to exchanges and there are also companies like Mediabrix who focus only on bringing brand advertising.

Diversifying through blacklists

Most ad-networks supports blacklists as a way for publishers to block certain advertisers from placing ads in their apps. This is mostly used for 2 things: 1) blocking competitors and 2) blocking inappropriate ad content. This feature however, can also be used to force ad-networks into skipping ads that are being shown too much. If you focus on the top 5 ads shown in your app and only allow one ad-network to serve them you will force the other ones to bring new ads and diversify the user experience.

Getting more visibility to what ads are being shown

While a solution to this problem might look far fetched at the moment, it’s actually feasible. The ad-networks are under a lot of pressure to be more transparent at the moment and this is one area where if each network gives up some transparency it can receive a lot in return. After all, ad-networks also loose from ad blindness. It will be a better world for everyone, publishers, advertisers, mediation platforms, ad-networks and users. However, someone needs to take the first step. Until then, feel free to contact SOOMLA if you want access to this kind of information. A side benefit of publishers gaining access to this info is that it will accelerate the path to full transparency by the ad-networks.

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App Monetization, Infographic

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This is the 3rd post in the series. The ad viewer of January is a user who made his app publisher very happy by generating the most amount of ad revenue for him when compared to other users. Our unique tech allows our customers to associate ad revenue to the user level and measure ad ltv. To check out previous months’ over achievers, follow this link for December and this one for November.

January Ad Viewer of the Month

This user alone generated 53.39 dollars for his app publisher in 20 activity days during January. What’s also interested is that he only recently started using the app – in mid December. The user contributed a bit over $20 in Dec. which makes his ARPU to date or his LTV to date $74. We are sure it will get even higher as he generates $2.66/day on average during January.

Attribue Ad Viewer of November
Country  United States
Device  iPad
Ad Types  Interstitial
Impresions  416
Active days 20
Revenue $53.39
eCPM $128.36
ARPDAU $2.66

IMG_4044

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App Monetization, Industry Forecasts

Latest report from App Annie supports the claim that the market is choosing View to Play as the business model of the future for the mobile ecosystem

Last November, while most of us were already preparing for the holidays, AppAnnie released a very interesting report that might have gone unnoticed by some of you. One of the Key Learnings is that Free-to-Play is giving way to View-to-Play. In other words, the fastest growing business model in the next 5 years in mobile apps will be in-app advertising and not in-app purchases.

In app advertising is growing at 24% CAGR and expected to surpass $110B by 2020 while Freemium is trailing behind

About App Annie and the report

The App Market data company needs no introduction from us and has become the source of data for most of the industry with regards to app store data. The company has over 600 employees in over 13, many of which are focused on researching data. From time to time, App Annie generates industry reports and forecasts and shares those through it’s blog and other content channels.
Company website – https://www.appannie.com/
Report Download Page – http://go.appannie.com/report-app-annie-app-monetization-2016-dg

What is View to Play?

If you haven’t heard the term View to Play, it’s probably because it’s new. When the app store just emerged, apps were sold and not given away for free. With the introduction of In-App Purchases, developer quickly started offering free apps to attract more users and find different ways to monetize them. This led to a new breed of game companies that specializes in conversion optimization, analytics, segmentation and performance marketing – the term Games as a Services was coined to reflect these new practices as well as Free to Play gaming. View to Play is similar in approach but instead of pushing users towards in-app purchases, the optimizations are focused around ad based monetization models – hence, “View to Play”. Users who want to advance in the game are often offered rewards and incentives for watching ads and a new breed of companies emerges with a toolset that includes special analytics capabilities around ad revenue measurement.

What is Driving the Change

In a recent article we covered how CPI is increasing and companies needs to adapt quickly. Well, some have already started and the App Annie report hints that more companies will be adopting the view to play model in the future.

These companies are realizing something that others have not. The CPI increase is highly correlated with the expected increase in ads LTV. They are both been driven by the same forces – the total mobile advertising spend is increasing twice as fast as the user growth. IAP revenues are actually increasing slower than the user growth and are becoming more and more concentrated in top grossing apps.

The cost per install is increasing over time as well as the average ad based revenue per user while In-App Purchase models are declining

This means that companies who transition quickly to view to play will be far better prepared for the future increase in CPIs. That is, as long as they can also adapt their measurement and optimization practices with a platform such as SOOMLA Traceback.

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App Monetization, Infographic

Ad viewer of December is the user who generated the most amount of ad revenue for his app publisher

We are continuing the series of Ad Viewer of the month that we started last month. This type of analysis is one of the things that sets SOOMLA apart. We are using the Traceback technology to provide publishers with reports that get as granular as a single user. The Ad Viewer of December is a single user who made the most amount of revenue for the publisher of the apps he was using. Here is the link for last month’s report – Ad Viewer of November

December Ad Viewer of the Month

The amount of ad revenue generated by this user is mind blowing – $52.92 generated for the app publishers. He registered 19 active days in the month of December and made an average of $2.78 in each one of them. Unlike the Ad Viewer of November, this user also received a lot of in-game rewards for his revenue contribution. His favorite ad-types were Offer Wall and Rewarded Video that surely gave him incentives for his ad interactions.

Attribue Ad Viewer of November
Country  United States
Device iPhone
Ad Types  Offer Wall, Rewarded Video
Impresions  398
Active days 19
Revenue $52.92
eCPM $132.98
ARPDAU $2.78

Infographic featuring the ad viewer of December and different attributes about him. How much revenue was generated and at what eCPM

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App Monetization, Video

Blog header image - what's inside the advertising black box - video snaps from casual connect panel
Last Casual Connect in Tel-Aviv introduced many interesting lectures and panels. However, this is the one when ad-networks secrets got revealed. These are the top 9 moments of the panel presented in an easy video navigation tool.

 

Panel Participants

Lior Shiff – Co-founder and ex-CEO, Product Madness

Guy Tomer – Co-founder and CMO, TabTale

Niko Vouri – Co-founder and COO, Rocket Games

Yaniv Nizan – Co-founder and CEO, SOOMLA

Noam Neuman – VP Mobile Strategy at Matomy

Fernando Pernica – Mobile Monetization at Ad-Colony

Minute 5:29 – The Secret Guage

Lior asks Fernando whether there is a way for ad-networks to dynamically manipulate rev-share rates for publishers and create periods where they are more competative. Can you gues the answer?

Minute 12:06 – What Surprised Yaniv

Lior asks Yaniv what surprised him the most when lifting the hood of the black box. Not all app users are made equal apparently.

Minute 14:30 – When Ad Networks get Naughty

Guy tells the story about an ad-network that didn’t play by the rules and showed inappropriate ads to kids user audience.

Minute 32:11 – Brands – Friend or Foe

When a big change comes along you can either get defensive or find the opportunities that change creates. While the entrance of brands to mobile ads makes buying users harder it creates new monetization opportunities that translates back into the ability to place more competitive CPI bids.

Minute 33:09 – Is there an Unbiased Mediation?

Why is the ownership of mediaiton by ad-networks a problem? Bias and lack of transparency come into play here.

Minute 35:54 – Ad Networks’ Transparency

Guy explains that regardless of their various attempts to get more data from the ad-networks they still couldn’t get granular data and even aggregated data is sometimes tough.

Minute 37:07 – Lack of Transparency is a Double Edged Sword

Fernando explains how mediation is a black box for the ad-networks and how the lack of transparency goes both ways.

Minute 39:53 – Are There Ad Whales?

Lior is asking Yaniv and Guy whether or not Ad Whales exist. Guy explains that he can’t track it today but Yaniv is answering with precision: “We have seen $124 generated by a single user”.

Minute 45:43 – How Would You Leverage Ad LTV Data

Yaniv is asking Niko what would he do differently if he had the power to know. Niko explains how granular ad revenue data can impact their user acquisition decisions.

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App Monetization

How can one single user generates $74 in ad revenue - here are some answers

I received the following questions yesterday and wanted to share the answers so more users can learn.

Being an indie game developer I’m trying to understand all the low level stuff that happens around user acquisition but I often lack experience to close the gap. After reading the latest blog post
(http://blog.soom.la/2017/01/ad-viewer-of-the-month-generates-74-at-134-ecpm-for-publisher.html,
I have a few simple questions that I hope you can answer. Regarding the user that generated $74 in ad revenue at $134 eCPM.

The reason of such a high eCPM

Does the advertiser know the spending potential of the user (whale) and his playing patterns and such wants to acquire it in a game where spending can be in tens of thousands of dollars?

First, let’s start with some basic terminology so that we will be aligned:

ADVERTISERS – App publishers who want to spend money and get users.
PUBLISHERS – App publishers who want to get money and are willing to put ads inside their games

Now, let’s talk about the reasons for high eCPM. In most games, the advertising transaction model is based on performance – CPC or CPI. Advertisers are willing to pay high CPI and CPC when they believe that users will likely to do two things:

  • be loyal/engaged/retained
  • spend money in their apps

Most likely in this case, the ad-network was able to convince some advertisers that a segment of users that includes this one is worthy of these high CPC or CPI but this by itself is not enough.

In order for high CPC or CPI to translate into high eCPM and revenue for the publisher, users needs to take actions – they need to engage with the ads, click on them and most likely also go and install the apps that were advertised to them.

In CPC model – eCPM is CPC bid x CTR
In CPI model – eCPM is CPI bid x CVR x CTR

There is an interesting report from Comscore that identifies a group of users they call “Heavy app downloaders” – you can read more about these users in this link – http://blog.soom.la/2016/12/reward-abusers-and-heavy-app-downloaders.html. It’s highly posible that the user who generated $74 in a single month was a “Heavy App Downloader”.

Was it a game where spending can be in tens of thousands of dollars?

Yes, as a general rule all big mobile game advertisers are apps where you can spend thousands of dollars.

Was this a result of retargeting?

Was the advertiser was trying to re-target the player which has churned? from the game, but did invest a big amount of money before churning?

It is possible that this user have been part of one or more retargeting campaigns. Obviously companies who have already experienced good results with this user would try to get him back. However, I doubt that this is enough. It takes more than one advertiser to generate that sort of revenue for the publisher and retargeting campaigns are only a small piece of the advertising ecosystem.

Are the advertisers wasting money on this user?

Regarding the 555 impressions delivered, if all these impressions are advertising a single game, I guess at some point the advertiser will stop Targeting ads to that player because it is a simple waste of money (he can not acquire the player). Is that correct?

Actually not correct.
Most ad-networks charge the advertiser based on a performance model CPI and CPC. This means that if the publisher earned $74 the specific user should have not only watched the ads but also clicked and installed some of the apps that were advertised to him. Most likely the publisher will want to keep getting this money so he will continue to serve ads to that users. The ad-network will keep targeting more ad campaigns to him due to the high performance and revenue he is generating for them.

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App Monetization

Ad viewer of the month header image featuring the user who made the most amoutn of ad revenue in the month of November

To demonstrate the pure awesomeness of TRACEBACK technology and it’s superiority over the alternatives we decided to start doing an “Ad Viewer of the Month” celebration. The idea is simple – we scan through all the users tracked through SOOMLA TRACEBACK and find the users who made his publisher the most amount of ad revenue. Unlike with In-App Purchases, these users are not taking this money out of their pockets but rather generate revenue for the app publishers by watching large amounts of ads and engaging with them.

November Ad Viewer of the Month

The user who did the most amount of ad revenue in November did no less than $74.76 for his app publisher. He used the app every single day in November – totaling 30 active days. More importantly, his eCPM and ARPDAU number are way off the charts and much higher than the averages for that app. Here is his score card

Attribue Ad Viewer of November
Country  United States
Device iPad
Ad Type Interstitial
Impresions 555
Active days 30
Revenue $74.76
eCPM $134.70
ARPDAU $2.49

Infographic showing details about the ad viewer for the month of november, this single user generated $74 for his publisher at $134 eCPM and $2.49 ARPDAU

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Analytics, App Monetization

Ad revenue concentration hero image with chart and text

We are happy to report some interesting data points we recently looked at. The goal was to understand how concentrated ad revenue really is. Everybody knows already that the 80/20 law applies in IAP – at least 80% of the revenue is driven by the top 20% of purchasers. There is plenty of research showing how concentrated IAP revenue is. Ad revenue, however, is still a mystery for most publishers and very few companies actually have the data on how concentrated the revenue is. If you take the naive approach and believe all the users contribute revenue based on the average eCPM you might think that the ad revenue concentration chart will be flat. The reality however, is very different.

Comparing Concentration in Ad Revenue vs IAP Revenue

In the image below you can see a comparison of the revenue concentration between ad based monetization and IAP based monetization. These charts are based on data from 28 days of activity in a Match-3 game where most of the monetization comes from interstitials. The revenue model behind the ad monetization is CPC in this case.

On the left side, the IAP revenue is highly concentrated and 80% of the revenue is generated by the top 20% of the users. The top user generated more than $300 in revenues for the app.

On the right side, we see that the ad revenue is also highly concentrated. The top 20% are contributing more than 50% of the revenue here and the top user generates $2.5 while there are users who only contribute a few cents.

Comparison between concentration of ad revenue and IAP revenue

Ad Revenue Concentration with Reward Ads

One of the hot trends of 2015 and 2016 was the adoption of rewarded video ads by many game publishers. We wanted to look at the ad revenue concentration in rewarded video as well. The chart below does exactly that.

The data here is from a single day so obviously more concentrated than information aggregated over an entire month. The game here is an mid-core action game and the monetization is done with both rewarded videos and an offer wall.

The ad revenue concentration is much higher in this data set. The top 20% of the users are contributing 90% of the revenue and the top user is contributing more than $15.

Ad revenue concentration in rewarded video ads and offer walls

Who are the users contributing high amounts of ad revenue

Once you realize that the ad revenue is concentrated almost as much as iAP revenue, your next question is likely to be: “how are these users”. On a high level, these users are typically the users who download many apps as indicated by a Comscore Report highlighted in this article. But you can go a lot further than that. Using SOOMLA Traceback you can profile these “Ad Whales and target them in marketing activities.

 

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Analytics, App Monetization

Reward Abusers written in blue text next to a trophy and Heavy App Downloaders written in green text next to an app icon with two axes

As the market adopts TRACEBACK technology we are learning new things about how users interact with ads. This allows us to classify users into types. Let’s think about these two types of users who are highly relevant to rewarded video based monetization.

Reward Abusers – these are users who watch the videos to get the rewards but are not contributing any revenue in Neither IAP nor in Ad revenue.

Heavy App Downloaders – these are users who download and try multiple apps each month. Typically, these are the users who end up generating the most amount of video ad revenue for your app.

How these segments impact your business

Let’s say you are buying traffic from a new source. You probably ask yourself, how many installs I received but you should also ask the million dollar question – “what type of users am I getting?”

Why?

Consider 2 possible sources:
Incent Campaign – this campaign gives users an incentive in another app in return for downloading your app. By nature these users are after the rewards so this source might be heavy in Reward Abusers
FB Campaign – now consider a campaign targeting lookalikes of users who are existing Heavy App Downloaders. This campaign is likely to bring more Heavy App Downloaders. You can learn more about this specific technique – here

How can you segment your users

If you are already convinced that knowing the Reward Abusers from the Heavy App Downloaders can impact your business your next question should be how to spot them. Let’s think about what features are similar and which ones are different between thm.
App Engagement – both user types have high app engagement
Video Ad Engagement – Reward Abusers will watch as much videos as Heavy App Downloaders
Post Impression Performance – this is the feature that sets them apart – Reward Abusers will only watch the videos while Heavy App Downloaders will also click and install the apps presented to them

Reward Abusers Heavy App Downloaders
App Engagement High High
Video Ad Engagement High High
Post Impression Performance Low High

So understanding what the user does after he watches the video ad is the key here. Today, there are two solutions in the market:
Developing In-house – this requires your engineering team to figure out specific ways to track post impression events with each ad-network and to keep updating the code every time there is an update to the ad-network SDKs
SOOMLA TRACEBACK – our platform does all the work for you, it requires a simple integration but once implemented you will be able to segment your users reliably, track ROAS and do many other mind blowing optimizations to your ad-revenue. CLICK TO LEARN MORE

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